A collaborative constraint-based meta-level recommender

ABSTRACT

Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications that employ means-end knowledge to map abstract user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases.

REFERENCES

• 1: Gediminas Adomavicius , Alexander Tuzhilin, Expert-Driven Validation of Rule-Based User Models in Personalization Applications, Data Mining and Knowledge Discovery, v.5 n.1-2, p.33-58, January-April 2001 [doi>10.1023/A:1009839827683]
• 2: Gediminas Adomavicius , Alexander Tuzhilin, Using Data Mining Methods to Build Customer Profiles, Computer, v.34 n.2, p.74-82, February 2001
• 3: Gediminas Adomavicius , Alexander Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, v.17 n.6, p.734-749, June 2005 [doi>10.1109/TKDE.2005.99]
• 4: T. L. Ainscough, T. E. DeCarlo, and T. W. Leigh. Building expert systems from the selling scripts of multiple experts. The Journal of Services Marketing, 10(4):23--40, 1996.
• 5: Marko Balabanović , Yoav Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [doi>10.1145/245108.245124]
• 6: R. Burke. Integrating knowledge-based and collaborative-filtering recommender systems. In AAAI-Workshop on Artificial Intelligence for E-Commerce, pages 69--72, 1999.
• 7: R. Burke. Knowledge-based recommender systems. Encyclopedia of Library and Information Systems, 69(2), 2000.
• 8: Robin Burke, Hybrid Recommender Systems: Survey and Experiments, User Modeling and User-Adapted Interaction, v.12 n.4, p.331-370, November 2002 [doi>10.1023/A:1021240730564]
• 9: R. Burke. Hybrid web recommender systems. In The Adaptive Web: Methods and Strategies of Web Personalization, pages 377--408, Heidelberg, Germany, 2007. Springer.
• 10: Robin D. Burke , Kristian J. Hammond , Benjamin C. Young, The FindMe Approach to Assisted Browsing, IEEE Expert: Intelligent Systems and Their Applications, v.12 n.4, p.32-40, July 1997 [doi>10.1109/64.608186]
• 11: Alexander Felfernig , Gerhard Friedrich , Dietmar Jannach , Markus Zanker, An Integrated Environment for the Development of Knowledge-Based Recommender Applications, International Journal of Electronic Commerce, v.11 n.2, p.11-34, Number 2 / Winter 2006-7 [doi>10.2753/JEC1086-4415110201]
• 12: William B. Frakes , Ricardo Baeza-Yates, Information retrieval: data structures and algorithms, Prentice-Hall, Inc., Upper Saddle River, NJ, 1992
• 13: Jonathan L. Herlocker , Joseph A. Konstan , Loren G. Terveen , John T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.5-53, January 2004 [doi>10.1145/963770.963772]
• 14: D. Jannach. Advisor suite - a knowledge-based sales advisory system. In L. S. Lopez de Mantaras, editor, 16th European Conference on Artificial Intelligence - Prestigious Applications of AI (PAIS), pages 720--724. IOS Press, 2004.
• 15: D. Jannach. Finding preferred query relaxations in content-based recommenders. In IEEE Intelligent Systems Conference (IS), pages 355--360, Westminster, UK, 2006. IEEE Press.
• 16: Kevin McCarthy , James Reilly , Lorraine McGinty , Barry Smyth, Experiments in dynamic critiquing, Proceedings of the 10th international conference on Intelligent user interfaces, January 10-13, 2005, San Diego, California, USA [doi>10.1145/1040830.1040871]
• 17: N. Mirzadeh, F. Ricci, and M. Bansal. Supporting user query relaxation in a recommender system. In 5th International Conference on E-Commerce and Web Technologies (EC-Web), pages 31--40, Zaragoza, Spain, 2004. Springer.
• 18: D. O`Sullivan, B. Smyth, and D. Wilson. Preserving recommenders accuracy and diversity in sparse datastes. International Journal of Artificial Intelligence Tools, 13(1):219--235, 2004.
• 19: D. Pham and S. S. Dimov. An effient algorithm for automatic knowledge acquisition. Pattern Recognition, 30(7):1137--1143, 1997.
• 20: F. Ricci and H. Werthner. Case base querying for travel planning recommendation. Information Technology and Tourism, 3:215--266, 2002.
• 21: Badrul Sarwar , George Karypis , Joseph Konstan , John Riedl, Analysis of recommendation algorithms for e-commerce, Proceedings of the 2nd ACM conference on Electronic commerce, p.158-167, October 17-20, 2000, Minneapolis, Minnesota, United States [doi>10.1145/352871.352887]
• 22: Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, p.285-295, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372071]
• 23: J. B. Schafer, D. Frankowski, and J. H. S. Sen. Collaborative filtering recommender systems. In The Adaptive Web: Methods and Strategies of Web Personalization, pages 291--324, Heidelberg, Germany, 2007. Springer.
• 24: M. Zanker and M. Jessenitschnig. Case-studies on exploiting explicit customer requirements in recommender systems. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, A. Tuzhilin and B. Mobasher (Eds.): Special issue on Data Mining for Personalization, (to appear 2008).
• 25: Markus Zanker , Markus Jessenitschnig , Dietmar Jannach , Sergiu Gordea, Comparing Recommendation Strategies in a Commercial Context, IEEE Intelligent Systems, v.22 n.3, p.69-73, May 2007 [doi>10.1109/MIS.2007.49]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Relevance feedback

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems

General Terms:
Algorithms

Keywords:
collaborative filtering, hybrid recommendation approaches, knowledge-based recommendation